The present invention is related to techniques and mechanisms for classifying senders of unsolicited bulk emails and the like. Additionally, it relates to filtering such unsolicited bulk emails based on such classification.
Users with email accounts typically receive daily unsolicited bulk email or “spam.” If unfiltered, spam can quickly inundate a user's inbox, thereby, wasting resources. Additionally, users can spend significant amounts of time culling unwanted spam from desired emails.
Spam filters exist for filtering spam based on the textual content within the email body. However, spammers continue to strive to defeat such filtering mechanisms using various techniques, such as obscuration of certain “spam-like” words with random characters (e.g., V$iagra), rendering the email body as an image, etc.
Other spam filters identify spam based one whether a particular IP address is blacklisted. A particular IP address is defined as a spammer by the recipients of the spam emails. Often, this recipient feedback can be significantly delayed since there may be a large user reaction time-lag. If an IP address starts sending a high volume of emails to users, and the content of the emails is not identifiable and the IP address has not been observed before, the email server typically delivers the emails. The filtering process then waits for the users to rate the emails as spam in order to potentially get enough certainty to block emails from the particular IP address. This process creates a window (during which the classifier waits for user feedback) during which spammers can send high volume of emails that are being delivered.
Accordingly, improved mechanisms for identifying senders of unsolicited bulk emails and the like would be beneficial.